The performance of energy management in hybrid electric vehicles is highly dependent on the forecasted velocity. To this end, a new velocity-prediction approach utilizing concept chaining neural network (CNN) introduced. This velocity forecasting subsequently used as basis for an equivalent consumption minimization strategy (ECMS). CNN to predict over different temporal horizons, exploiting information provided through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)...

Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in real-world setting. Moreover, it time-consuming to manually label huge amounts data due uncertainties driver behavior and variances among the analysts. To address this problem, semisupervised approach, support vector machine (S3VM), employed classify drivers into aggressive normal styles based on few points. First,...

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution environmental pollution and fuel savings. The benefit of the is generally realized as amount consumption saved, which by itself represents challenge develop right energy management strategies (EMSs) for HEVs. Moreover, meeting design requirements are essential optimal power distribution at price conflicting objectives. To this end, significant number EMSs proposed in literature, require categorization method better...

Many people die each year in the world single vehicle roadway departure crashes caused by driver inattention, especially on freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, which, lane detection key issue. In this paper, after brief overview of existing methods, we present robust algorithm based geometrical model and Gabor filter. This two assumptions: road front approximately planar marked which are often correct highway freeway where most accidents...

Driving style analysis plays a pivotal role in intelligent vehicle design. This paper presents novel framework for driving based on primitive patterns. To this end, Bayesian nonparametric approach hidden semi-Markov model (HSMM) is introduced to extract the patterns from muti-dimensional time-series data without prior knowledge of these For approach, hierarchical Dirichlet process (HDP) applied learn unknown smooth dynamical modes HSMM, called Two other types approaches (HDP-HMM and sticky...

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle intelligent vehicle systems. A wide range of both mathematical identification methods are presented from the control point view in this paper based on driving data, such as brake/throttle pedal position steering wheel angle, among others. Subsequently, driver’s characteristics derived model embedded into advanced assistance evaluation verification...

Misunderstanding of driver correction behaviors is the primary reason for false warnings lane-departure-prediction systems. We proposed a learning-based approach to predict unintended lane-departure and chances drivers bring vehicles back lane. First, personalized model lane-keeping behavior established by combining Gaussian mixture hidden Markov model. Second, based on this model, we developed an online model-based prediction algorithm forthcoming vehicle trajectory judge whether will act...

Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems avoiding collisions. In this paper, we focus on intent in car-following scenarios from perception-decision-action perspective according his/her driving history. A learning-based inference method, using onboard data CAN-Bus, radar, cameras as explanatory variables, introduced infer drivers' braking decisions by combining Gaussian mixture model (GMM) with hidden Markov (HMM). The GMM used...

To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for steering is proposed. be specific, novel dynamic strategy the ratio of systems that treats speed, lateral deviation, yaw error, angle as inputs driver's expected output developed. determine parameters proposed strategy, drivers are classified into three types according level sensitivity errors, i.e., low, middle, high. The HCFC offers (HCSS) with tunable gain,...

Accurately predicting the changes in speed has a significant impact on quality of energy management hybrid vehicles. Many methods for have been proposed literature, but few fully consider vehicle dynamics to predict changes. To this end, new method is introduced and perform vehicles situations where lateral plays role. Based tire-road friction coefficient GPS signal, maximum cornering vehicle, which each tire force does not saturate, evaluated. Then, principle using less braking more...

Abstract Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties terrain. In most systems, researchers assume a priori knowledge terrain properties, or both. forest environments, vegetation such as trees, shrubs, bushes has that vary with change seasons, age, species. addition, surface often rough, sloped, and/or covered layer grass, vegetation, snow. The complexity environment presents challenges for autonomous...

A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve of driving styles, a k-means clustering-based support vector machine (kMC-SVM) method developed used for classifying drivers into two types: aggressive moderate. First, vehicle speed throttle opening are treated as feature parameters reflect styles. Second, discriminate driver behaviors reduce number vectors, clustering extract gather types data...

Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving of path-tracking behaviors for different divers, statistical pattern-recognition method is developed to deal with the uncertainty or characteristics based probability density estimation. First, describe driver styles, speed throttle opening are selected as discriminative parameters, conditional kernel function built, respectively, two representative e.g., aggressive normal....

This paper is concerned with the speed synchronization optimal controller design problem for clutchless automated manual transmission systems in electric vehicles. It well known that one of main challenges a system. In vehicles, are regarded as promising devices and generally required to have high-precision capabilities. order satisfy this requirement, robust control scheme proposed paper. The law consists preview control, integral state-feedback control. Using an augmentation method,...

The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers' driving style. This paper proposes a driving-style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) order improve fuel economy for HEVs. For this purpose, first, statistical pattern recognition approach proposed classify drivers into six groups from moderate aggressive using kernel density estimation and entropy theory. Then, the effects style on...

Learning-based methods have gained increasing attention in the intelligent vehicle community for developing highly autonomous vehicles and advanced driving assistance systems (ADAS). However, traditional offline learning lack ability to adapt individual behavior. To overcome this limitation, a combined framework (CLF) based on Natural Actor Critic (NAC) general regression neural network (GRNN) is developed paper. GRNN can be trained historical data, while NAC carried out online. In way,...

Interpretation of common-yet-challenging inter- action scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge specific with predefined models, limiting adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete Dirichlet stochastic processes to reveal underlying interaction patterns the ego vehicle other nearby Our model relaxes...

Longitudinal motion controllers based on over- simplified models result in steady-state errors, oscillations, and overshoots of the velocity, all which impair unmanned ground vehicle (UGV) multiple objectives (trajectory tracking capability, energy economy, ride comfort). While it is challenging for complicated methods to accomplish real-time control vehicle-mounted electronic unit (ECU), meets harsh working conditions but has limited computing power. This paper proposes a multi-objective...

In the life of Mongolian people, warhorses are undoubtedly best friends and capable assistants nomadic tribes. Horses indispensable in military campaigns, hunting, daily life. They allow nomads to gallop freely across vast northern grasslands. As an essential means transportation, horses hold immense significance. The Mongolians known as "horseback nation," so what artifact can represent pinnacle craftsmanship context arts crafts? Naturally, one thinks saddles. Saddles necessities for our...

The Mongolian peoples living and dining utensils have unique regional characteristics, with a history spanning over thousand years. dietary of nomadic are even more diverse in shape pattern, rich symbolic meanings that reflect their aspirations for better life emotional thoughts. Each ethnic group uses its familiar language to write sing about own culture, creating oral literature, songs, dances, ornaments, vessels, patterns, all which embody the wisdom respective groups. These elements an...